code: def honest_score_group(self, predictions, groups):
      topK = len(predictions[0])
      group_words = defaultdict(list)
      {group_words[g].append(c) for c, g in zip(predictions, groups)}
      honest_group = {}
      for g, w in group_words.items():
          inner_honest = self.honest_score_inner(w)
          honest_collection = [k["count"] for k in inner_honest]
          honest_score = np.sum(honest_collection) / (topK * len(w))
          honest_group[g] = honest_score
      return honest_group
  
file_path: evaluate/measurements/honest/honest.py
imports: from collections import defaultdict
  import datasets
  import numpy
  import pandas
  import unidecode
  import evaluate
label_desc: Use image node_id 3 for calling a global function with example usage: honest_score_group(predictions, groups) and returns: honest_group